This paper presents a method for estimating the population variability distribution of the failure parameter (failure rate or failure probability) for each failure mode considered in PSA (Probabilistic Safety Assessment). We focus on the utilization of generic estimates from various industry compendia for the estimation. The estimates are complicated statistics of failure data from plants. When the failure data referred in two or more sources are overlapped, dependency occurs among the estimates provided by the sources. This type of problem is first addressed in this paper. We propose methods based on ML-II estimation in Bayesian framework and discuss the characteristics of the proposed estimators. The proposed methods are easy to apply in real field. Numerical examples are also provided.